A collaborative framework for structure identification over print documents

Maeda F. Hanafi, Miro Mannino, Azza Abouzied

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe Texture, a framework for data extraction over print documents that allows end-users to construct data extraction rules over an inferred document structure. To effectively infer this structure, we enable developers to contribute multiple heuristics that identify different structures in English print documents, crowd-workers and annotators to manually label these structures, and end-users to search and decide which heuristics to apply and how to boost their performance with the help of ground-truth data collected from crowd-workers and annotators. Texture's design supports each of these different user groups through a suite of tools.We demonstrate that even with a handful of student-developed heuristics, we can achieve reasonable precision and recall when identifying structures across different document collections.

Original languageEnglish (US)
Title of host publicationProceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450367912
DOIs
StatePublished - Jul 5 2019
Event2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019 - Amsterdam, Netherlands
Duration: Jul 5 2019 → …

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019
CountryNetherlands
CityAmsterdam
Period7/5/19 → …

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ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Hanafi, M. F., Mannino, M., & Abouzied, A. (2019). A collaborative framework for structure identification over print documents. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019 [a8] (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3328519.3329131